Digital phenotyping and the (data) shadow of Alzheimer's disease

Milne, Richard; Costa, Alessia and Brenman, Natassia F.. 2022. Digital phenotyping and the (data) shadow of Alzheimer's disease. Big Data & Society, 9(1), ISSN 2053-9517 [Article]

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Abstract or Description

In this paper, we examine the practice and promises of digital phenotyping. We build on work on the ‘data self’ to focus on a medical domain in which the value and nature of knowledge and relations with data have been played out with particular persistence, that of Alzheimer's disease research. Drawing on research with researchers and developers, we consider the intersection of hopes and concerns related to both digital tools and Alzheimer's disease using the metaphor of the ‘data shadow’. We suggest that as a tool for engaging with the nature of the data self, the shadow is usefully able to capture both the dynamic and distorted nature of data representations, and the unease and concern associated with encounters between individuals or groups and data about them. We then consider what the data shadow ‘is’ in relation to ageing data subjects, and the nature of the representation of the individual's cognitive state and dementia risk that is produced by digital tools. Second, we consider what the data shadow ‘does’, through researchers and practitioners’ discussions of digital phenotyping practices in the dementia field as alternately empowering, enabling and threatening.

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RM and AC's work was supported by Welcome Trust grants 213579 and 206194. NB's work was supported by an ESRC Postdoctoral Fellowship and grant (MR/N029941/1) from the National Institute for Health Research (NIHR) and the Medical Research Council (MRC).


Data shadow, digital phenotype, data double, digital health, ageing, Alzheimer’s disease

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11 January 2022Published Online
January 2022Published

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Date Deposited:

25 Jan 2022 11:27

Last Modified:

25 Jan 2022 11:28

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Yes, this version has been peer-reviewed.


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